Energy-based Latent Aligner for Incremental Learning

K. J. Joseph*, Salman Khan, Fahad Shahbaz Khan, Rao Muhammad Anwer, Vineeth N. Balasubramanian

*Tämän työn vastaava kirjoittaja

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference contributionScientificvertaisarvioitu

3 Sitaatiot (Scopus)


Deep learning models tend to forget their earlier knowledge while incrementally learning new tasks. This behavior emerges because the parameter updates optimized for the new tasks may not align well with the updates suitable for older tasks. The resulting latent representation mismatch causes forgetting. In this work, we propose ELI: Energy-based Latent Aligner for Incremental Learning, which first learns an energy manifold for the latent representations such that previous task latents will have low energy and the current task latents have high energy values. This learned manifold is used to counter the representational shift that happens during incremental learning. The implicit regularization that is offered by our proposed methodology can be used as a plug-and-play module in existing incremental learning methodologies. We validate this through extensive evaluation on CIFAR-100, ImageNet subset, ImageNet 1k and Pascal VOC datasets. We observe consistent improvement when ELI is added to three prominent methodologies in class-incremental learning, across multiple incremental settings. Further, when added to the state-of-the-art incremental object detector, ELI provides over 5% improvement in detection accuracy, corroborating its effectiveness and complementary advantage to the existing art. Code is available at:

Otsikko2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
ISBN (elektroninen)978-1-6654-6946-3
DOI - pysyväislinkit
TilaJulkaistu - 2022
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaIEEE Conference on Computer Vision and Pattern Recognition - New Orleans, Yhdysvallat
Kesto: 18 kesäk. 202224 kesäk. 2022


NimiIEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN (painettu)1063-6919


ConferenceIEEE Conference on Computer Vision and Pattern Recognition
KaupunkiNew Orleans


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